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Updated readme, added hf paper links
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README.md
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title: Intrinsic
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emoji: 🐢
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sdk: gradio
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sdk_version: 5.0.1
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license: cc-by-nc-sa-4.0
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title: Intrinsic
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sdk: gradio
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license: cc-by-nc-sa-4.0
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# Intrinsic Image Decomposition
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This space contains a demo for the following papers:
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**Colorful Diffuse Intrinsic Image Decomposition in the Wild**, [Chris Careaga](https://ccareaga.github.io/) and [Yağız Aksoy](https://yaksoy.github.io), ACM Transactions on Graphics, 2024 \
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[Project](https://yaksoy.github.io/ColorfulShading/) | [Paper](https://yaksoy.github.io/papers/TOG24-ColorfulShading.pdf) | [Supplementary](https://yaksoy.github.io/papers/TOG24-ColorfulShading-supp.pdf) | [HF](https://huggingface.co/papers/2409.13690)
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**Intrinsic Image Decomposition via Ordinal Shading**, [Chris Careaga](https://ccareaga.github.io/) and [Yağız Aksoy](https://yaksoy.github.io), ACM Transactions on Graphics, 2023 \
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[Project](https://yaksoy.github.io/intrinsic/) | [Paper](https://yaksoy.github.io/papers/TOG23-Intrinsic.pdf) | [Video](https://www.youtube.com/watch?v=pWtJd3hqL3c) | [Supplementary](https://yaksoy.github.io/papers/TOG23-Intrinsic-Supp.pdf) | [Data](https://github.com/compphoto/MIDIntrinsics) | [HF](https://huggingface.co/papers/2311.12792)
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We propose a method for generating high-resolution intrinsic image decompositions for in-the-wild images. Our method consists of multiple stages. We first estimate a grayscale shading layer using our ordinal shading pipeline. We then estimate low-resolution chromaticity information to account for colorful illumination effects while maintaining global consistency. Using this initial colorful decomposition, we estimate a high-resolution, sparse albedo layer. We show that our decomposition allows us to train a diffuse shading estimation network using only a single rendered indoor dataset.
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![representative](https://github.com/compphoto/Intrinsic/blob/main/figures/representative.png?raw=true)
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Our estimated components unlock multiple illumination-aware editing operations such as per-pixel white balancing and specularity removal:
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![applications](https://github.com/compphoto/Intrinsic/blob/main/figures/app_teaser2.jpg?raw=true)
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